Global businesses spent $93.5 billion on AI solutions in 2018, up from $67.85 billion the year before. In comparison, up to 80% of AI initiatives fail, and only 11% of businesses that use AI see a meaningful return on their investment.
Does this prove AI is nothing more than an empty promise?
The reverse is true. Extremely high failure rates and drawn-out ROIs for AI initiatives may be traced back to the fact that very few businesses understand how to use AI on a systemic level. According to Deloitte, businesses with an established AI strategy have a ROI of more than 5% on their AI investments, with an average payback time of 1.2 years.Meanwhile, businesses that naively adopt AI technologies often struggle to turn a profit. So, before using AI in your company, what should you keep in mind?
Five Strategies for Implementing AI in Your Organization
There are essentially five ways in which we might outline your strategy for using AI:
- Learn about AI and its potential uses.
- Focus on achievable objectives while using AI.
- Analyze your company’s current position on the AI and digital maturity scales.
- Take baby steps into the world of AI while keeping an eye on the future.
- Strive for the highest level of AI performance.
Step 1: Familiarize yourself with AI and its capabilities.
Machines and programs that do jobs more easily or automatically and use their own intellect to do so are said to have artificial intelligence. In order to do this, AI requires data, which may either be manipulated in advance for algorithmic analysis or used in its raw form. Complexity and intelligence may be found throughout the spectrum of AI forms:
It’s a kind of machine learning with human supervision. Such algorithms thrive on annotated data, becoming particularly adept at a narrow range of tasks, such as, say, distinguishing between filled and empty parking spaces.
Machine learning without human supervision Unsupervised ML solutions save software programmers time by eliminating the need for labeled data during algorithm training.Instead, experts just need to describe the content of databases, and algorithms may then self-train.
Learn by doing, or reinforcement learning. In reinforcement learning, developers release algorithms into the world without specifying how the algorithms should analyze input. By providing a solution to a problem, these AI systems may be tested for accuracy by their creators. If they are not happy with the outcomes, they may tweak the AI’s performance settings even further.
Learning at a very deep level Deep learning solutions are now standard in state-of-the-art CV and NLP software because they are powered by deep neural networks, which are artificial neural networks with many layers of neurons that can assess data against numerous criteria.
Each AI is best at a different set of tasks, like figuring out what review site users think or figuring out why your business’s energy costs went up all of a sudden.
However, you won’t be able to streamline all of your business’s processes and duties using AI from the outset, reducing your operating expenses to zero. Most of the time, human specialists should look at how well algorithms work and step in quickly, such as when operating complex industrial equipment or figuring out if tumors are benign or cancerous.
Step 2: Establish some attainable, concrete objectives for your AI implementation.
Identify the business challenges you hope AI can help you address, then link those problems to measurable results before using AI.
A comprehensive assessment of your processes across all departments and roles should thus be the first step on your road toward using AI. Hire professional technology consultants and business analysts if you need assistance.
Talk to both internal and external stakeholders, including business unit leaders and C-suite executives. Building a proof-of-concept (POC) prototype of the AI solution is another option for winning over the C-suite.
In addition, your AI goals should be SMART (specific, measurable, attainable, relevant, and time-bound). Intelligent process automation (IPA) solutions allow you to do things like automate 70% of customer job jobs using NLP-powered chatbots or process insurance claims three times quicker.
3. Evaluate how artificial intelligence-ready your business is.
Check that your company has these AI building blocks in place so that it can reach the goals listed in Step 2. Hire outside help from an AI consulting business if you don’t have any in-house IT experts with relevant expertise in AI development. Another option for getting started with AI in business is to buy a no-code or low-code SaaS solution that includes AI features; however, you will still likely need to bring in software developers to put it up.
Algorithms that employ AI learn from the data your business already collects and apply that knowledge to help you right now. This information may be organized (that is, kept in data warehouses and completely ready for analysis) or unstructured (i.e., resides in data lakes and lakehouses or external platforms in the form of sensor readings, audio and video files, images, or uneditable documents). Nearly 90% of the information created by a typical business is unstructured. Using this information for training algorithms and automating routine tasks requires a solid data storage, retrieval, and analytics infrastructure, ideally one that is hosted in the cloud.
Materials for use in computers In reference to the cloud, companies like Google, Amazon, and Microsoft provide the tools necessary to develop and launch AI-based applications. To put it another way, cloud computing is essential for implementing AI in the workplace. Your AI transition will need a solid foundation if your company is still using insular, old apps that are incompatible with current technology stacks.
The success or failure of your AI implementation depends on the people working on it, so keep them in mind as you plan. As resistance to change is a common reason why digital transformation projects fail, it’s important to plan ahead for staff training and onboarding, as well as for dealing with the ethical challenges that AI in the workplace presents.
The Force Field Analysis is a tool for weighing the benefits and drawbacks of introducing AI into an organization. Before diving into the analysis, it’s important to do a thorough review of your organization’s processes and most valuable assets, as well as an analysis of the internal and external factors that affect your operations.
Each aspect helping or limiting the spread of AI should be given a score before the study is run. A positive or negative cumulative score shows if you are ready to start using AI in business or if you first need to update your IT infrastructure and procedures.
4. Implement AI on a small scale at first, with expansion in mind.
The standard procedure for implementing AI in a corporate setting is to begin with a limited set of tasks, then expand the scope of the project as learnings and insights are gained, the algorithm’s performance is evaluated, and user input is gathered. It’s also important to learn from your errors and share that information with the rest of your company.
However, new information from Gartner suggests that just 53% of corporate AI initiatives make it beyond the prototype stage and into production. It’s possible that the lackluster outcomes are due to the fact that most businesses treat their AI initiatives as standalone POCs rather than developing strategic strategies to implement AI throughout the organization.
As a result, it’s crucial to plan out your AI adoption strategy in advance and determine where AI fits into your organization’s bigger picture of tech. Don’t be afraid to move on to other use cases if a certain AI proof of concept fails to deliver within three months.
5. Strive towards perfection in AI.
Finally, how do you determine whether an AI project is successful and whether it should be scaled? An error-free AI implementation plan includes the following:
Ensure that your company has solid data management (you may need to partner with a reputable big data consulting firm for this).
Bringing together all of your organization’s different software systems to create a single data environment.
With the help of outside consultants, AI experts working in-house at an AI excellence center can learn new techniques, improve the performance of existing algorithms, and try out ideas that are completely new.
Establishing a framework for your company that allows for the optimization and synchronization of processes on an ongoing basis and across all of your departments, upper management, and ordinary people
Conclusion
Finally, even if your early artificial intelligence experiments don’t pan out, you should keep at it. Although technology has been around for quite some time, it is continually evolving and improving at a dizzying rate. When AI is used in business, it requires a big shift in how people think, and early adopters of AI will need to go through a big digital and organizational change to get the most out of this revolutionary technology.
